| Abstract: |  This thesis deals with the motion planning for formations of robots and helicopters, where
the task is to find a feasible (and possibly optimal) trajectory for all the entities of the formations.
A Model Predictive Control (MPC) approach is used to find the optimal trajectories considering the kinematics
constraints of the formations.
The optimization is defined as a problem of Sequential Quadratic Programming (SQP), which need considerable amount of time to solve.
To speed up the optimization, it is useful to provide an feasible initial solution for the SQP solver.
We propose to use the sampling-based motion planning techniques like Rapidly Exploring Random trees (RRT), for this task.
The advantage of the RRT approach is, that the kinematics motion constraints are considered during
construction of the initial trajectory.
This helps the SQP solver to find the trajectory in a less amount of time.
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